A supply chain network may have one or more locations that receive items from suppliers and distribute within a supply chain network in order to meet the service level requirement for each location. These items may be raw materials, subassemblies or finished goods. Service level targets are typically measured at a finished goods level to meet customer demands. These service level targets are dependent on the inventory kept across the entire supply chain, including the components and subordinates used to make the finished goods. The objective of minimizing total supply chain cost, however, conflicts with typical service level constraints. Because variability exists at all levels in the supply chain, this is a stochastic problem. For example, variability may occur at forecast errors in finished goods at the customer level, distribution lead-time variability in the network, or variability in the manufacturing processes, vendor service variability for procured items. Therefore, computing optimal safety stock and inventory targets to cover all these variabilities in a connected supply chain is a complex stochastic domain problem for any inventory planner.
Prior solutions are incapable of performing optimization for inventory throughout the entire bill-of-material, and are instead limited to merely the finished goods level. Such computations fail to consider the variability that exists through the components and subordinates level. Furthermore, such computations fail to suggest the right safety stock or inventory targets at bill-of-material levels. As a result, users of such computation, stock higher finished goods, but may often be “out-of-sock.” Therefore, prior solutions have proven inadequate.